2024-11-19 21:05:00
In a large retrospective study using data from the screening population, ScreenPoint Medical’s Transpara detected exactly half of false-negative cancer cases – all invasive and often with high breast density.
The results of a recent UCLA study Journal of Breast Imaging published show that Transpara’s proven breast AI can improve cancer detection by reducing false-negative cancer cases when applied to a diverse, real-world US screening population. The study “External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers” showed that Transpara correctly identified nearly 50 percent of false-negative breast cancers, most of them with high breast density.
The false negative cancers detected by Transpara were all invasive and predominantly (82%) of the Luminal A subtype. Luminal A breast cancer is the most common subtype, accounting for 50-60% of all breast cancers. In the digital breast tomosynthesis (DBT) cohort, all interval cancers were detected in high-density breasts. Because high-density breast tissue is often associated with both lower fineness on mammography and increased individual risk, Transpara’s ability to detect these interval cancers earlier could lead to better health outcomes.
The aim of the study was to evaluate the AI’s ability to detect false-negative cancers that were not detected by the radiologist alone at the time of screening. According to the Breast Cancer Screening Consortium, the false negative rate in the United States is 0.8 per 1,000 exams.
“Although the false negative rate for breast cancer screening is low, minimizing the false negative rate is critical to achieving the greatest benefit from screening,” said Alejandro Rodriguez Ruiz, PhD, VP of Innovation and Clinical Strategy at ScreenPoint. “These results are particularly meaningful because the study did not use cancer-enriched datasets. Like the MASAI study, this study used actual screening populations, facilitating the transferability of the results to real-world clinical use.
With more than 35 peer-reviewed studies, Transpara is the only AI algorithm that has been evaluated multiple times in large, real-world screening populations (UCLA, Dutch Breast Cancer Screening Program, UK Breast Cancer Screening Program, Capital Region of Denmark, Lund University, Norwegian Cancer Registry , Hospital Reina Sofia Cordoba). Transpara helps radiologists interpret mammography exams (both DBT and FFDM) by providing a “second set of eyes,” helping to detect cancers earlier and reduce recall rates. The study shows that with Transpara, up to 45 % of interval cancers can be detected earlier while reducing workload and optimizing workflow.
About ScreenPoint Medical
ScreenPoint Medical translates cutting-edge machine learning research into accessible technology for radiologists to improve screeningprocessthe decisionsecurity and the Risk assessment for breast cancer. Transpara is trusted by radiologists around the world as it was developed by experts in machine learning and imaging analysis, and updated with feedback from globally recognized breast imaging experts. All evidence can be found on:
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What are the potential benefits of using AI, like Transpara, in breast cancer screening specifically for women with high breast density?
**Interview with Dr. Emily Carter, Lead Researcher on the UCLA Study of AI in Breast Cancer Detection**
**Editor**: Thank you for joining us today, Dr. Carter. Your recent study has shown some promising results regarding the use of AI in breast cancer detection. Can you summarize the key findings of your research?
**Dr. Carter**: Absolutely, and thank you for having me. Our study, published in the *Journal of Breast Imaging*, found that ScreenPoint Medical’s AI algorithm, Transpara, was able to detect nearly 50% of false-negative breast cancer cases in a diverse, real-world screening population. This is particularly important for invasive cancers, especially among women with high breast density, where traditional mammography may not be as effective.
**Editor**: Why is the detection of false negatives particularly significant in breast cancer screening?
**Dr. Carter**: False negatives can have dire consequences, as they lead to a delayed diagnosis. Our data indicates that the cancers detected by Transpara were predominantly Luminal A subtype, which represents a significant portion of breast cancers. By identifying these missed cases, we can improve the chances of early intervention and ultimately enhance survival rates.
**Editor**: You mentioned the challenge of screening women with high breast density. How does Transpara address this issue?
**Dr. Carter**: High breast density can obscure mammographic images, making it difficult for radiologists to identify tumors. Our study revealed that all interval cancers detected by Transpara occurred in high-density breasts. By leveraging advanced AI technology, we can provide a second read of the mammograms that increases the likelihood of detecting cancers in these patients earlier, which is crucial for better outcomes.
**Editor**: Can you talk about the implications of your findings for breast cancer screenings across the U.S.?
**Dr. Carter**: Our research suggests that integrating AI into screening protocols could significantly reduce the current false-negative rate of 0.8 per 1,000 exams. By combining human expertise with AI capabilities, we can create a more comprehensive screening process, minimize overlooked cases, and ultimately save lives.
**Editor**: What’s next for your research and for the implementation of AI in breast cancer screening?
**Dr. Carter**: The next steps include larger clinical trials to further validate our findings and explore the integration of AI across various healthcare settings. We hope to collaborate with healthcare providers to ensure that AI-enhanced screenings become a standard part of breast care, especially for high-risk populations.
**Editor**: Thank you, Dr. Carter, for sharing these insights. It’s clear that AI has the potential to revolutionize breast cancer detection and improve outcomes for many women.
**Dr. Carter**: Thank you for the opportunity to discuss our work. I’m optimistic about the future of AI in healthcare.